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ecdf

Compute empirical cumulative distribution function (ecdf) for baseline and target data specified for data drift detection

    Description

    example

    E = ecdf(DDiagnostics) returns the table E, which stores the ecdf values for all the variables specified for drift detection in the call to the detectdrift function.

    ecdf returns NaN values for categorical variables.

    example

    E = ecdf(DDiagnostics,Variables=variables) returns the table E for the variables specified by variables.

    Examples

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    Generate baseline and target data with two variables, where the distribution parameters of the second variable change for the target data.

    rng('default') % For reproducibility
    baseline = [normrnd(0,1,100,1),wblrnd(1.1,1,100,1)];
    target = [normrnd(0,1,100,1),wblrnd(1.2,2,100,1)];

    Perform permutation testing for any drift between the baseline and target data.

    DDiagnostics = detectdrift(baseline,target)
    DDiagnostics = 
      DriftDiagnostics
    
                  VariableNames: ["x1"    "x2"]
           CategoricalVariables: []
                    DriftStatus: ["Stable"    "Drift"]
                        PValues: [0.2850 0.0030]
            ConfidenceIntervals: [2×2 double]
        MultipleTestDriftStatus: "Drift"
                 DriftThreshold: 0.0500
               WarningThreshold: 0.1000
    
    
      Properties, Methods
    
    

    Compute the ecdf values for all variables.

    E = ecdf(DDiagnostics)
    E=2×3 table
                    x             F_Baseline         F_Target   
              ______________    ______________    ______________
    
        x1    {201×1 double}    {201×1 double}    {201×1 double}
        x2    {201×1 double}    {201×1 double}    {201×1 double}
    
    

    E is a table with two rows and three columns. The two rows correspond to the two variables, x1 and x2. For each variable, ecdf computes the ecdf values over a common domain for the baseline and target data. The function stores the common domain for each variable in the column x, the ecdf values for the baseline data in the column F_Baseline, and the ecdf values for the target data in the column F_Target.

    Access the ecdf values for variable 2 in the baseline data.

    E.F_Baseline{2}
    ans = 201×1
    
             0
        0.0100
        0.0100
        0.0200
        0.0300
        0.0400
        0.0500
        0.0600
        0.0700
        0.0800
          ⋮
    
    

    Plot the ecdf values of the baseline and target data for variable x2.

    stairs(E.x{2},E.F_Baseline{2},LineWidth=1.5)
    hold on
    stairs(E.x{2},E.F_Target{2},LineWidth=1.5)
    title('ECDF for x2')
    xlabel('x2')
    ylabel('Empirical CDF')
    legend('Baseline','Target',Location='east')
    hold off

    The plot of the ecdf values also shows the drift in the distribution of the target data.

    Load the sample data.

    load humanactivity

    For details on the data set, enter Description at the command line.

    Assign the first 1000 observations as baseline data and the next 1000 as target data.

    baseline = feat(1:1000,:);
    target = feat(1001:2000,:);

    Test for drift on all variables.

    DDiagnostics = detectdrift(baseline,target);

    Compute the ecdf values for only the first five variables.

    E = ecdf(DDiagnostics,Variables=[1:5])
    E=5×3 table
                     x             F_Baseline          F_Target    
              _______________    _______________    _______________
    
        x1    {2001×1 double}    {2001×1 double}    {2001×1 double}
        x2    {2001×1 double}    {2001×1 double}    {2001×1 double}
        x3    {2001×1 double}    {2001×1 double}    {2001×1 double}
        x4    {2001×1 double}    {2001×1 double}    {2001×1 double}
        x5    {2001×1 double}    {2001×1 double}    {2001×1 double}
    
    

    Access the ecdf values for the third variable in the baseline data.

    E.F_Baseline{3}
    ans = 2001×1
    
             0
             0
             0
             0
             0
             0
        0.0010
        0.0020
        0.0030
        0.0040
          ⋮
    
    

    Plot the ecdf values of the baseline and target data for variable x3.

    stairs(E.x{3},E.F_Baseline{3},LineWidth=1.5)
    hold on
    stairs(E.x{3},E.F_Target{3},LineWidth=1.5)
    title('ECDF for x3')
    xlabel('x3')
    ylabel('Empirical CDF')
    legend('Baseline','Target',Location = 'southeast')
    hold off

    The ecdf plot shows the drift in the target data for variable x3.

    Input Arguments

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    Diagnostics of the permutation testing for drift detection, specified as a DriftDiagnostics object returned by detectdrift.

    List of variables for which to compute the ecdf values, specified as a string array, cell array of character vectors, or list of integer indices.

    Example: Variables=["x1","x3"]

    Example: Variables=(1,3)

    Data Types: single | double | char | string

    Output Arguments

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    ecdf values for all variables specified for drift detection in the call to detectdrift, returned as a table with the following columns.

    Column NameDescription
    xCommon domain over which to evaluate the empirical cdf
    F_Baselineecdf values for the baseline data
    F_Targetecdf values for the target data

    For each variable in E, the columns store x and the ecdf values in cell arrays. To access the values, you can index into the table; for example, to obtain the ecdf values for the second variable in the baseline data, use E.F_Baseline{2,1}.

    Version History

    Introduced in R2022a